I. Field
The following description relates generally to wireless communication systems and more particularly to improving performance of channel estimations.
II. Background
Wireless communication systems have become a prevalent means by which a majority of people worldwide have come to communicate. These systems may be multiple-access systems capable of supporting communication with multiple users by sharing the available system resources (e.g., bandwidth and transmit power).
A typical wireless communication system or network (e.g., employing frequency, time, and/or code division techniques) includes one or more base stations that provide a coverage area and one or more mobile (e.g., wireless) terminals that can transmit and receive data within the coverage area. A typical base station can concurrently transmit multiple data streams for broadcast, multicast, and/or unicast services, wherein a data stream is a stream of data that can be of independent reception interest to a mobile terminal. A mobile terminal within the coverage area of a base station can be interested in receiving one, more than one, or all the data streams carried by the composite stream. Likewise, a mobile terminal can transmit data to the base station and/or another mobile terminal.
In Orthogonal Frequency Division Multiplexing (OFDM) communication systems (e.g., Long Term Evolution (LTE), Ultra Mobile Broadband (UMB), and so forth), channel estimation algorithms include frequency-domain interpolation approach, a Discrete Fourier Transform (DFT) based approach, or a Minimum Mean Square Error (MMSE) based approach. For example, in a DFT-based approach, pilot tones extracted from frequency domain samples are converted to the time domain utilizing Inverse Fast Fourier Transform (IFFT). Time domain truncation, tap-processing, and zero-padding are performed and a conversion back to the frequency domain is conducted using a Fast Fourier Transform (FFT). The process of IFFT, zero-padding, and FFT is equivalent to the optimal sync interpolation in frequency. Thus, the DFT-based approach generally provides better performance compared to direct frequency-domain interpolation approach. MMSE-based approach is generally a better approach in the sense that it determines channel taps in order to minimize the mean-square error. Therefore, the MMSE-based approach generally performs better than the DFT-based approach.
The DFT-based approach can have undesirable performance on a subset of tones (e.g., edge tones, center tones, and so forth) and the MMSE-based approach can have high implementation complexity due to large-size matrix multiplication. Further, both approaches can have problems associated with performance under the presence of other-cell interference using orthogonal sequence (OS). Therefore, there exists a need to overcome the aforementioned as well as other problems.